Various methods of automatic detection and recognition of predetermined features from sensor data sets are known to the skilled addressee. For example, automatic license plate recognition (ALPR) from digital photography is presently used in several applications, including speed monitoring and infringement and toll management. In prior art methods, ALPR is usually accomplished using three processing steps, illustrated in FIGS. 1 to 3, after an image of a vehicle has been acquired. Firstly, the region of the license plate in the image is determined and a data set obtained including the data of the license plate (FIG. 1); secondly, the characters on the license plate are segmented for individual processing (FIG. 2); and thirdly, optical character recognition (OCR) techniques are employed on each segmented character to determined each character (FIG. 3).
The first step may be performed using a number of known techniques, including colour detection, signature analysis, edge detection, and so on. Any inclination from the horizontal line in the captured image is determined and the image rotated before it becomes ready for character recognition module. The image may also be further processed to remove noise.
For segmentation, a known histogram method may be used, where each character is labelled in the license plate image, and then each label is extracted. Each character in the plate is extracted in a single image and normalized prior to the recognition step.
With particular reference to FIG. 3, an example of an optical character recognition process is illustrated for determining the characters in the license plate of FIG. 1. To begin with, the segmented characters are first normalized and then fed into a neural network for character recognition. A back propagation feed forward Neural Network consisting of two layers has been selected. The input character size was resized to 20×50 pixels resulting in 1000 inputs to the input layer of neural networks. The output layer consists of 36 neurons each corresponding to one symbol of the alphanumeric character set. The neural network is then trained on 90 samples of noisy alphanumeric characters. As a result of the training, the output neuron corresponding to a certain character should give a value higher than the values of the other neurons when the same character is the input of the neural networks as shown in FIG. 3. For the neural network, each node in the output layer is associated with one character Ck with outputs which vary from 0 to 1, this value corresponding to frame n. The neural network outputs are normalized and used as estimates of the a posteriori probability of each character:
      p    ⁡          (                        C          k                /                  f          n                    )        =            C      k                      ∑                  i          =          1                36            ⁢              C        i            
For this prior art technique to work well, the quality of the acquired image must be of a level that allows a relatively clear photograph to be taken to increase the accuracy of the OCR techniques employed. This tends not to be an issue on open roads during daylight hours or under well lit street lighting. However, there are many situations where such optimum conditions are not available, such as at night time on roads with no or poor street lighting, during wet weather, in car parks, under bridges or in poorly lit tunnels. In such conditions, aforementioned prior art techniques may require the use of relatively expensive cameras which can operate in a variety of lighting conditions, and/or the use of additional lighting or flashes at the time of taking the photograph to illuminate the subject of the image being acquired. Also, error levels of such known methods have shown that about 1 in 5 license plates are incorrectly determined. There is a desire in the technical field to reduce this error.